Using chest velocity to process physiological signals to remove chest compression artifacts

ABSTRACT

A method of analyzing a physiological (e.g., an ECG) signal during application of chest compressions. The method includes acquiring a physiological signal during application of chest compressions; acquiring the output of a sensor from which information on the velocity of chest compressions can be determined; and using the information on the velocity to reduce at least one signal artifact in the physiological signal resulting from the chest compressions.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser.No. 10/786,359, filed Feb. 24, 2004, which application is acontinuation-in-part of and claims priority from U.S. application Ser.No. 10/704,366, filed on Nov. 6, 2003, now issued U.S. Pat. No.7,220,235, and both are hereby incorporated by reference.

TECHNICAL FIELD

This invention relates to devices for assisting cardiac resuscitation.

BACKGROUND

Resuscitation treatments for patients suffering from cardiac arrestgenerally include clearing and opening the patient's airway, providingrescue breathing for the patient, and applying chest compressions toprovide blood flow to the victim's heart, brain and other vital organs.If the patient has a shockable heart rhythm, resuscitation also mayinclude defibrillation therapy. The term basic life support (BLS)involves all the following elements: initial assessment; airwaymaintenance; expired air ventilation (rescue breathing); and chestcompression. When all three (airway breathing, and circulation,including chest compressions) are combined, the term cardiopulmonaryresuscitation (CPR) is used.

Current automated ECG rhythm analysis methods interrupt cardiopulmonaryresuscitation (CPR) to avoid artifacts in the ECG resulting from chestcompressions. Long interruptions of CPR have been shown to result inhigher failure rate of resuscitation. Studies have reported that thediscontinuation of precordial compression can significantly reduce therecovery rate of spontaneous circulation and the 24-hour survival rate.Y. Sato, M H. Weil, S. Sun, W. Tang, J. Xie, M. Noc, and J. Bisera,Adverse effects of interrupting precordial compression duringcardiopulmonary resuscitation, Critical Care Medicine, Vol. 25(5),733-736 (1997). Yu et al., 2002. Circulation, 106, 368-372 (2002), T.Eftestol, K. Sunde, and P A. Steen, Effects of Interrupting PrecordialCompressions on the Calculated Probability of Defibrillation SuccessDuring Out-of-Hospital Cardiac Arrest, Circulation, 105, 2270-2273,(2002).

Management of breathing is another important aspect of the CPR process.Typical methods of monitoring breathing employ some form of impedancepneumography which measure and track changes in the transthoracicimpedance of the patient. Currently, however, chest compressions resultin significant artifact on the impedance signals, resulting inimpedance-based pneumographic techniques as unreliable indicators oflung volume during chest compressions.

Adaptive filters have been attempted as a way of removingchest-compression artifacts in the ECG signal. S O. Aase, T. Eftestol, JH. Husoy, K. Sunde, and PA. Steen, CPR Artifact Removal from Human ECGUsing Optimal Multichannel Filtering, IEEE Transactions on BiomedicalEngineering, Vol. 47, 1440-1449, (2000). A. Langhelle, T. Eftestol, H.Myklebust, M. Eriksen, B T. Holten, P A. Steen, Reducing CPR Artifactsin Ventricular Fibrillation in Vitro. Resuscitation. March; 48(3):279-91(2001). J H. Husoy, J. Eilevstjonn, T. Eftestol, S O. Aase, H Myklebust,and P A. Steen, Removal of Cardiopulmonary Resuscitation Artifacts fromHuman ECG Using an Efficient Matching Pursuit-Like Algorithm, IEEETransactions on Biomedical Engineering, Vol 49, 1287-1298, (2002). H R.Halperin, and R D. Berger, CPR Chest Compression Monitor, U.S. Pat. No.6,390,996 (2002). Aase et al. (2000) and Langhelle et al. (2001) usedthe compression depth and thorax impedance as reference signals fortheir adaptive filter. Husoy et al. (2002) extended this study by usinga matching pursuit iteration to reduce the computational complexity;however, their results are usually computationally intensive, such asinvolving the calculation of a high order inverse filter. Halperin etal. (2002) proposed a frequency-domain approach using the auto- and thecross-spectrum of the signals and a time-domain approach using arecursive least square method for adaptive filtering the ECG signal. Inboth approaches, intensive computations are required.

There are numerous references available on adaptive filters. E.g., S.Haykin, Adaptive Filter Theory, Third Edition, Upper Saddle River, N.J.,USA. Prentice-Hall, 1996

SUMMARY

In general the invention features a method of analyzing a physiological(e.g., an ECG) signal during application of chest compressions. Themethod includes acquiring a physiological signal during application ofchest compressions; acquiring the output of a sensor from whichinformation on the velocity of chest compressions can be determined; andusing the information on the velocity to reduce at least one signalartifact in the physiological signal resulting from the chestcompressions.

Preferred implementations of the invention may incorporate one or moreof the following: The physiological signal may be any of a variety ofphysiological signals, including an ECG signal, an IPG signal, an ICGsignal, or a pulse oximetry signal. The sensor may be a velocity sensor,and the information on the velocity may be determined from the velocitysensor. The sensor may be an accelerometer, and the information on thevelocity may be determined from integration of the output of theaccelerometer. Using the information on the velocity to reduce at leastone signal artifact in the physiological signal may comprise timealigning the physiological signal with the velocity. Using theinformation on the velocity to reduce at least one signal artifact inthe physiological signal may comprise using an adaptive filter that maybe adjusted to remove chest compression artifacts. The method mayinclude a ventricular fibrillation detection algorithm for processingthe physiological signal with reduced artifact to estimate whether aventricular fibrillation may be present. The method may include apreprocessing step that detects when chest compressions are applied andautomatically initiates the adaptive filter. The method may includeenabling delivery of a defibrillation shock if the algorithm estimatesthat ventricular fibrillation is present. A difference signal may beproduced, the difference signal being representative of the differencebetween the physiological signal fed into the adaptive filter and thephysiological signal after artifact reduction by the adaptive filter.The difference signal may provide a measure of the amount of artifact inthe physiological signal. The difference signal may be used to modifythe subsequent processing of the physiological signal. If the differencesignal indicates that the amount of artifact exceeds a first threshold,the ventricular fibrillation detection algorithm may be modified to makeit more resistant to being influenced by the artifact. If the differencesignal indicates that the amount of artifact exceeds a second thresholdhigher than the first threshold, use of the ventricular defibrillationdetection algorithm may be suspended. Spectral analysis may be performedon the difference signal, and adjustments may be made to filtering ofthe physiological signal based on the outcome of the spectral analysis.The velocity signal may undergo a normalization pre-processing prior tobeing fed to an adaptive filter. The adaptive filter may include an FIRfilter. The adaptive filter may include a zero-th order filter. Theadaptive filter may have coefficients that are dynamically controlled byan estimate of the physiological signal. The adaptive filter may havethe capability of being automatically reset when the difference betweenthe filter output and the measured physiological signal is beyond athreshold. The automatic reset may be capable of dynamically changingthe step size and thus improving the relationship of convergence andstability of the filter. A time-aligning process may be performed on thephysiological and velocity signals, wherein the time aligning processaligns the two signals relative to the compressions. The method mayinclude adaptive filtering of the output of the time aligning process,wherein the adaptive filtering reduces the error between thephysiological and velocity signals. The adaptive filter may include aKalman filter. The adaptive filter may employ adaptive equalization.

Among the many advantages of the invention (some of which may beachieved only in some of its various implementations) are the following:

This invention provides excellent techniques for (a) adaptively removingthe artifacts induced by CPR in an ECG signal, (b) enhancing an ECGsignal for monitoring, and (c) increasing the reliability of ECG rhythmadvisory algorithms.

As part of a rhythm advisory algorithm, various implementations of theinvention could be incorporated in an ECG monitor, an externaldefibrillator, an ECG rhythm classifier, or a ventricular arrhythmiadetector.

The invention makes it possible to continue performing CPR while ECGdata is collected for an ECG rhythm advisory algorithm. This can enhancethe result of CPR, leading, for example, to an increase in the successrate of resuscitation.

The invention can also provide a “cleansed” ECG signal output fordisplay to the user of a defibrillator.

The invention also provides for the first time a means of measuring lungvolume during chest compressions by impedance-based methods. The methodmay also be used to filter other physiological signals corrupted bycompression-induced artifact, such as impedance cardiography and pulseoximetry.

This invention demonstrates excellent performance at removing the CPRartifact with a zero-th order FIR filter, thus making someimplementations of the invention much simpler and faster than theadaptive-filter structures proposed in the prior art.

Pre-processing of the reference signal and an automatic-reset featuremake it possible for some implementations of the invention to use arelatively large step size for adaptation, thus making convergencefaster and more stable.

Some implementations of the invention achieve excellent performance inCPR-artifact removal at reduced computational cost.

Other features and advantages of the invention are described in thedetailed description, drawings, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one implementation of the invention.

FIG. 2 shows plots of the ECG signal, CPR reference signal, and outputof adaptive filter for a normal sinus rhythm.

FIG. 3 shows plots of the ECG signal, CPR reference signal, and outputof adaptive filter for ventricular fibrillation.

FIG. 4 is a block diagram of a filtered-X least mean squares (FXLMS ANC)algorithm.

FIG. 5 is a block diagram of an implementation using the algorithm ofFIG. 4.

FIG. 6 shows two spectral power distributions related to theimplementation of FIG. 5.

DETAILED DESCRIPTION

There are a great many possible implementations of the invention, toomany to describe herein. Some possible implementations that arepresently preferred are described below. It cannot be emphasized toostrongly, however, that these are descriptions of implementations of theinvention, and not descriptions of the invention, which is not limitedto the detailed implementations described in this section but isdescribed in broader terms in the claims.

One possible implementation is illustrated by a flow chart in FIG. 1.The front end of an AED acquires both the ECG signal and the CPR signal,which is the velocity of compression of the chest. If chest displacementor acceleration are measured instead of velocity, velocity can bemathematically acquired via one or more integration or differentiationoperations from the measurement signal.

The velocity signal undergoes pre-processing, and is then fed to anadaptive filter. In a preferred implementation, the pre-processing is anormalization of the velocity signal so that the signal supplied to theadaptive filter is limited to be within 0 and 1. But normalization isnot required. In another implementation, a time-aligning process isperformed on the ECG and the reference signal by such methods ascross-correlation. This provide alignment of the two signals relative tothe compressions so that the input signals of the adaptive filter arebetter aligned. But this aligning process is not required. Otherpreprocessing can be applied to the velocity signal to improve theperformance of the adaptive filter.

In FIG. 1, x(n) and y(n) are the input and the output of the adaptivefilter H, which can be an FIR filter, an IIR filter, or another type offilter. In a preferred implementation, the coefficients of the filterare dynamically controlled by the estimated ECG signal:h(n)=h(n−1)+m×e(n)×X(n)where h(n) is a vector containing the filter coefficients, m is a vectorcontaining the step sizes for each filter coefficients, e(n) is theestimated ECG signal, and X(n) is a vector containing the input data.The estimated ECG signal is computed by subtracting the filter outputy(n) from the measured ECG signal (containing artifact).

In some implementations, there is an automated resetting mechanism. Whenthe difference between the filter output y(n) and the measured ECG s(n)is beyond a threshold, the adaptive filter will reset its coefficientsso that the system will not become unstable.

Other filter structures than the one shown in FIG. 1, as well as othermathematical representations of the filtering, are possible.

FIG. 2 shows samples of the performance of the adaptive filter of FIG. 1in response to a normal sinus rhythm. The signal in (a) is the ECGsignal with CPR artifact. The signal in (b) is the compression velocityused as the reference signal. The signal in (c) is the output of theadaptive filter.

FIG. 3 shows samples of the performance of the adaptive filter of FIG. 1during ventricular fibrillation. The signal in (a) is the ECG signalwith CPR artifact. The signal in (b) is the compression velocity used asthe reference signal. The signal in (c) is the output of the adaptivefilter.

As shown in both FIG. 2 and FIG. 3, the implementation of FIG. 1 is ableto suppress the CPR artifacts embedded in the measured ECG signals (a).The CPR artifact is nearly, if not completely, removed in the estimatedECG signal (c). The velocity signal (b) used as a reference signal isclearly correlated with the CPR artifacts in the measured ECG signals(a).

The adaptive filter assumes that the artifact in the signal iscorrelated with the reference signal and uncorrelated with the desiredsignal (estimated ECG). It thus adaptively estimates the artifact usingthe reference signal and subtracts the estimated artifact from themeasured ECG signal.

The results shown in FIG. 2 are based on a 0th-order FIR filter, whichsimply scales the current sample of the ECG signal adaptively. The CPRartifact was significantly reduced, if not completely removed. Thisimplementation thus combines simplicity and efficiency in itsperformance.

In the applications of adaptive filters, the speed of adaptationconvergence is usually controlled by a step-size variable. A fasterconvergence requires a larger step size, which usually tends to make thefilter less stable. The automatic resetting mechanism of someimplementations can dynamically change the step size and thus improvethe relation of convergence and stability.

The coefficients of the filter are updated in a sample-by-sample manner.The changes of the coefficients, i.e., h(n)-h(n−1) is proportional tothe product of the step size and the reference signal. The amplitude ofthe reference signal can thus affect the stability and convergence ofthe filter. The pre-processing of the reference signal can thereforeenhance the performance of the filter by adjusting the reference signal.

In another implementation, a time-aligning process is performed on theECG and velocity signals by such methods as cross-correlation. Thisprovide alignment of the two signals relative to the compressions. Then,preferably, adaptive filtering methods are used such as those involvedin the minimization of the mean-squared error between the ECG and thevelocity.

A processing unit could be provided for detecting when compressions arebeing applied and automatically turning on the adaptive filter. Theoutput of the adaptive filter (i.e., the ECG signal with artifactreduced) could be supplied to a ventricular fibrillation (VF) detectionalgorithm (e.g., a shock advisory algorithm) of an automatic externaldefibrillator (AED).

An error signal could be produced that is representative of thedifference between the ECG input and ECG output of the adaptive filter.This error signal would give a measure of the amount of CPR artifact inthe signal, and it would be useful as a means of modifying thesubsequent processing of the ECG. For instance, if the artifact levelgets high enough (e.g., higher than a first threshold), the VF detectionalgorithm thresholds could be increased to make it more resistant to anyCPR artifact that still remained in the ECG signal. If the level goteven higher (e.g., higher than a second threshold higher than the firstthreshold), the VF detection could be shut off entirely.

In preferred implementation, the filter output is presented graphicallyon the display of a defibrillator or other medical device incorporatingan electro-cardiographic function. The filter output may also be printedon a strip-chart recorder in the medical device. Alternatively, thefilter output may provide the input signal for subsequent signalprocessing performed by the processing means. The purpose of such signalprocessing may take the form of QRS detection, paced beat detectionduring pacing, arrhythmia analysis, and detection of ventricularfibrillation or other shockable rhythms.

Spectral analysis could be performed on the error signal, and based onthe major bands of frequency content of the error signal, thepre-filtering of the ECG signal prior to the VF detection can beadjusted. For instance, if the error signal is found to reside primarilyin the 3-5 Hz band, additional filtering can be provided in that bandprior to input into the VF detection (or other ECG processing)algorithm.

Many other implementations of the invention other than those describedabove are within the invention, which is defined by the followingclaims.

For example, methods of adaptive channel equalization may be employed toameliorate both synchronization and phase errors in the velocitywaveform. Kalman filtering techniques may also be employed to improveperformance of the filter when rescuer performance of chest compressionschanges over time and is better modeled as a non-stationary process.

Time alignment of the ECG and velocity signal may also be accomplishedby such methods as cross-correlation techniques known to those skilledin the art. This will provide alignment of the two signals relative tothe compressions. Then, preferably, adaptive filtering methods are usedsuch as those involved in the minimization of the mean-squared errorbetween the ECG and the velocity.

In a further implementation, more sophisticated signal processingmethods may be used to minimize ECG artifacts induced by CPR chestcompressions. For example, methods known as feed forward active noisecancellation (FANC) may be used. FIG. 4 shows a block diagram of thefiltered-X least mean squares (FXLMS ANC) algorithm, as developed byWidrow and Burgess. P(z) represents the unknown plant through which thesignal x(n) is filtered. Digital filter W(z) is adaptively adjusted tominimize the error signal e(n). In one implementation, as depicted inFIG. 5, x(n) is the unfiltered ECG signal, P(z) is eliminated from thediagram, and d(n) is approximated with the chest compression velocitysignal v(n). In the LMS algorithm, assuming a mean square cost functionξ(n)=E[e2(n)], the adaptive filter minimizes the instantaneous squarederror, ξ(n)=e2(n), using the steepest descent algorithm, which updatesthe coefficient vector in the negative gradient direction with step sizeμ:w(n+1)=w(n)−μ/2*Ñξ(n),where Ñξ(n) is an instantaneous estimate of the mean square error (MSE)gradient at time n equal to −2v(n) e(n). Stability and accuracy of theFXLMS ANC algorithm can be improved by adding a variable cutoff low passfilter H(z) to eliminate frequency components in the ECG not related tothe chest compression artifact. In general, the spectral energy of thechest compression artifact is predominately lower than those of the ECG.A cutoff frequency of approximately 3 Hz is adequate in many cases, butthis may vary from patient to patient and among different rescuersperforming chest compressions. To overcome this difficulty, an FFT isperformed on v(n) and input into a cutoff frequency estimation (CFE)procedure that determines the optimal cutoff frequency, fC, for thelowpass filter. In a preferred implementation, the decision is based oncalculating the frequency, not to exceed 5 Hz, below which 80% of thewaveform energy is present, but this percentage may vary and additionaldecision logic may be employed. For instance, an FFT may also becalculated for x(n), also input to the CFE procedure. By firstnormalizing amplitude of the frequency spectra X(z) amplitude peak ofthe compression artifact and then subtracting the velocity spectra V(z)from the normalized input X′(z), the difference spectra is calculatedΔX′ (z)=X′(z)−V′(z). Frequencies are then determined for V(z) and ΔX′(z)at which most of the spectral energy is within, set in this embodimentto 97%, and labeled fCV and fCX, respectively, and shown in FIG. 6. FCis then set to the lesser of fCV and fCX. Alternatively, fC can be setto some intermediate frequency between fCV and fCX.

The quality of other physiological signals, such as impedancecardiographic (ICG), impedance pneumographic (IPG), or pulse oximetry,known to those skilled in the art, may also be also be enhanced by thefilter, particularly if the sensor is located on the thoracic cage innearby proximity to the motion sensor from which the velocity signal isderived. Minimization of compression artifact with impedancepneumography signals can be accomplished with any of the previouslydescribed methods.

The adaptive filter can be used to minimize the cross-correlation of theadaptive-filter output with the reference signal or thecross-correlation of the adaptive-filter output with the measured ECGsignal.

The invention claimed is:
 1. A device for analyzing an impedancecardiographic (ICG) physiological signal during application of chestcompressions, the device comprising: circuitry for acquiring a impedancecardiographic physiological signal from a impedance cardiographic sensorapplied to the chest during application of chest compressions; circuitryfor acquiring the output of a motion sensor applied to the chest fromwhich information on the velocity of chest compressions can bedetermined, and processing circuitry for using the information on thevelocity to reduce at least one signal artifact in the impedancecardiographic signal resulting from the chest compressions.
 2. A devicefor analyzing an impedance pneumographic physiological signal duringapplication of chest compressions, the device comprising: circuitry foracquiring a impedance pneumographic physiological signal from aimpedance pneumographic sensor applied to the chest during applicationof chest compressions; circuitry for acquiring the output of a motionsensor applied to the chest from which information on the velocity ofchest compressions can be determined, and processing circuitry for usingthe information on the velocity to reduce at least one signal artifactin the impedance pneumographic signal resulting from the chestcompressions.
 3. The device of claim 1 or 2 wherein the motion sensor isa velocity sensor, and the information on the velocity is determinedfrom the velocity sensor.
 4. The device of claim 1 or 2 wherein themotion sensor is an accelerometer, and the information on the velocityis determined from integration of the output of the accelerometer. 5.The device of claim 1 or 2 wherein using the information on the velocityto reduce at least one signal artifact in the physiological signalcomprises time aligning the physiological signal with the velocity. 6.The device of claim 1 or 2 wherein using the information on the velocityto reduce at least one signal artifact in the physiological signalcomprises using an adaptive filter that is adjusted to remove chestcompression artifacts.
 7. The device of claim 6 wherein the processingcircuitry is configured to provide a preprocessing step that detectswhen chest compressions are applied and automatically initiates theadaptive filter.
 8. The device of claim 6 wherein the processingcircuitry is configured to produce a difference signal, the differencesignal being representative of the difference between the physiologicalsignal fed into the adaptive filter and the physiological signal afterartifact reduction by the adaptive filter.
 9. The device of claim 8wherein the difference signal provides a measure of the amount ofartifact in the physiological signal.
 10. The device of claim 9 thedifference signal is used to modify the subsequent processing of thephysiological signal.
 11. The device of claim 10 wherein spectralanalysis is performed on the difference signal, and adjustments are madeto filtering of the physiological signal based on the outcome of thespectral analysis.
 12. The device of claim 6 wherein the velocity signalundergoes a normalization pre-processing prior to being fed to anadaptive filter.
 13. The device of claim 6 wherein the adaptive filtercomprises an FIR filter.
 14. The device of claim 13 wherein the adaptivefilter comprises a zero-th order filter.
 15. The device of claim 6wherein the adaptive filter comprises coefficients that are dynamicallycontrolled by an estimate of the physiological signal.
 16. The device ofclaim 6 wherein the adaptive filter comprises the capability of beingautomatically reset when the difference between the filter output andthe measured physiological signal is beyond a threshold.
 17. The deviceof claim 16 wherein the automatic reset comprises the capability ofdynamically changing the step size and thus improving the relationshipof convergence and stability of the filter.
 18. The device of claim 1 or2 further comprising a time-aligning process performed on thephysiological and velocity signals, wherein the time aligning processaligns the two signals relative to the compressions.
 19. The device ofclaim 18 further comprising adaptive filtering of the output of the timealigning process, wherein the adaptive filtering reduces the errorbetween the physiological and velocity signals.
 20. The device of claim6 wherein the adaptive filter comprises a Kalman filter.
 21. The deviceof claim 6 wherein the adaptive filter employs adaptive equalization.